Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle
DOI:
https://doi.org/10.26555/jiteki.v16i1.16949Keywords:
Autonomous Vehicle, Haar Like Detection, Image Processing, Region of Interest, Road DetectionAbstract
Nowadays, an autonomous vehicle is one of the fastest-growing technologies. In its movements, the autonomous vehicle requires a good navigation system to run on the specified lane. One sensor that is often used in navigation systems is the camera. However, this camera is constrained by the process and its reading, especially to detect roads that are suitable for the vehicle's position. Thus, this research was conducted to detect the road and distance of nearby objects using the HSV color space method. From the test results, this research succeeded in detecting roads with an accuracy of 78.012 %, and an accuracy of 80% for the safe/unsafe area detection. The results also showed that the method achieved an accuracy of 80% and 74.76%for object detection and object distance detection, respectively. The results of this research implied that the HSV method wasquite good with fairly high accuracy to detect roads and vehicles.References
J. Baili et al., “Lane departure detection using image processing techniques,†in 2017 2nd International Conference on Anti-Cyber Crimes (ICACC), 2017, pp. 238–241. DOI: 10.1109/Anti-Cybercrime.2017.7905298
P. Bhope and P. Samant, “Use of Image Processing in Lane Departure Warning System,†in 2018 3rd International Conference for Convergence in Technology (I2CT), 2018, pp. 1–4. DOI: 10.1109/I2CT.2018.8529819
J. He, H. Rong, J. Gong, and W. Huang, “A lane detection method for lane departure warning system,†in 2010 International Conference on Optoelectronics and Image Processing, 2010, vol. 1, pp. 28–31. DOI: 10.1109/ICOIP.2010.307
S.-H. Jang, D.-J. Yoon, J.-H. Kim, and B.-W. Kim, “Research of object classification algorithm based on LIDAR for UGV,†in 2011 11th International conference on control, automation and systems, 2011, pp. 746–749. Online
Z. Dong, W. Li, and Y. Zhou, “An autonomous navigation scheme for UAV in approach phase,†in 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), 2016, pp. 982–987. DOI: 10.1109/CGNCC.2016.7828919
V. Bobkov, S. Melman, A. Kudrashov, and A. Scherbatyuk, “Vision-based navigation method for a local maneuvering of the autonomous underwater vehicle,†in 2017 IEEE Underwater Technology (UT), 2017, pp. 1–5. DOI: 10.1109/UT.2017.7890304
N. M. Ali, N. Khair, A. Rashid, and Y. M. Mustafah, “Performance comparison between RGB and HSV color segmentations for road signs detection,†Applied Mechanics and Materials, vol. 393, no. 1, pp. 550-555, 2014. DOI: 10.4028/www.scientific.net/AMM.393.550
H. S. V. C. Space and I. Automatica, “Road signs recognition using a dynamic pixel aggregation technique in the HSV color space,†in Proceeding 11th International Conference on Image Analysis and Processing, pp. 572-577, 2001. DOI: 10.1109/ICIAP.2001.957071
X. Hu and C. He, “Moving object detection algorithm based on Gaussian Mixture Model and HSV space,†Current Journal of Applied Science and Technology, vol. 14, no. 6, pp. 1–8, 2016. DOI: 10.9734/BJAST/2016/24249
X. Shi, B. Kong, and F. Zheng, “A new lane detection method based on feature pattern,†in 2009 2nd International Congress on Image and Signal Processing, 2009, pp. 1–5. DOI: 10.1109/CISP.2009.5304294
G. Zhi-feng, W. Bo, D. Ming-jie, and S. Yong-sheng, “Intelligent real-time lane detection for vehicles in urban streets,†2012. DOI: 10.1049/cp.2012.2277
A. F. Cela, L. M. Bergasa, F. L. Sanchez, and M. A. Herrera, “Lanes detection based on unsupervised and adaptive classifier,†in 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks, 2013, pp. 228–233. DOI: 10.1109/CICSYN.2013.40
K. B. Kim and D. H. Song, “Real time road lane detection with RANSAC and HSV Color transformation,†Journal of Information and Communication Convergence Engineering, vol. 15, no. 3, pp. 187–192, 2017. DOI: 10.6109/jicce.2017.15.3.187
J. Huang, B. Kong, B. Li, and F. Zheng, “A new method of unstructured road detection based on HSV color space and road features,†in 2007 International Conference on Information Acquisition, pp. 596–601, 2007. DOI: 10.1109/ICIA.2007.4295802
E. Reinhard and T. Pouli, "Colour Spaces for Colour Transfer. In: Schettini R., Tominaga S., Trémeau A. (eds) Computational Color Imaging," CCIW 2011. Lecture Notes in Computer Science, vol 6626. Springer, Berlin, Heidelberg, 2011. DOI: 10.1007/978-3-642-20404-3_1
S. N. Endah, R. Kusumaningrum, and H. A. Wibawa, “Color space to detect skin image: the procedure and omplication,†Sci. J. Informatics, vol. 4, no. 2, pp. 143–149, 2017. DOI: 10.15294/sji.v4i2.12013
D. Ding, C. Lee, and K. Lee, “An adaptive road ROI determination algorithm for lane detection,†2013 IEEE Int. Conf. IEEE Reg. 10 (TENCON 2013), pp. 1–4, 2013. DOI: 10.1109/TENCON.2013.6718807
R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,†in Proceeding International Conference on Image Processsing, pp. 0–3, 2002. DOI: 10.1109/ICIP.2002.1038171
P. A. Rosyady and R. Sumiharto, “Highway Visual Tracking System using Thresholding and Hough Transform,†J. Ilmu Tek. Elektro Komput. dan Inform., vol. 4, no. 2, p. 93, 2018. DOI: 10.26555/jiteki.v4i2.12016
S. Choudhury, S. P. Chattopadhyay, and T. K. Hazra, “Vehicle detection and counting using haar feature-based classifier,†in 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 106–109, 2017. DOI: 10.1109/IEMECON.2017.8079571
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